AEs are usually used for clean signals and to denoise the noisy signals, denoising AEs (DAE) are introduced by Vincent et al.[1]} where noise is added to the input data and designed such that more robust features can be extracted than the AE algorithm. Figure 1 shows a schematic of DAE. Input vector \(x \in {[0,1]}^d\)  is given to the system, where d is the input vector's dimension. The initial input x is corrupted to \(\tilde{x}\)  by a stochastic mapping \(\tilde{x} \sim {C(\tilde{x}|x)}\) , which partially destroys the input data, as per destruction rate. The algorithm uses the corrupted \(\tilde{x}\)  as input data and then maps it to the corresponding \(y_i\)  and ultimately to its reconstruction \(z_i\) .
